Dynamic network selection using kernels

ABSTRACT

A method for determining whether to perform vertical handoff between multiple network. The method comprises obtaining a plurality of selection metrics for each network, calculating, for each of the other communication networks, a predicted utility value from at least the corresponding plurality of selection metrics using a variable kernel regression function, obtaining, for the current communication network, a second plurality of selection metrics; calculating a second predicted utility value for the current communication network from at least the corresponding second plurality of selection metrics using a second variable kernel regression function, comparing each of the predicted utility values for each of the plurality of other communication networks with the second predicted utility value and switching to one of the other communication networks having the highest predicted utility value, if the highest predicted utility value is greater than the second predicted utility value.

RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.11/982,004, filed Oct. 31, 2007, which claims priority to U.S.Provisional Application No. 60/855,709 filed on Oct. 31, 2006.

FIELD OF THE INVENTION

This invention relates to mobile communication devices, mobile networkmanagements, and handoff between multiple networks. More particularly,the invention relates to a method of determining whether to switch to adifferent network.

BACKGROUND

Mobile communication devices are commonly used in today's society. Mostof these devices are capable of telecommunication using at least onenetwork. Many of the newer mobile devices are now capable oftelecommunication using multiple networks. The ability to switch betweenmultiple networks in an efficient manner is essential for these newerdevices. Future multiple networks are expected to combine severaldifferent radio-access technologies, such as 3G cellular, WLAN, andWiMax. This variety of access options gives a user with amulti-interface device the possibility of being “always best connected”,using ‘vertical’ handoffs between the heterogeneous networktechnologies.

Vertical handoff is the process by which a mobile device switchesbetween two different networks.

Traditional handover algorithms are based on a single attribute, signalstrength, and handover policies are threshold based. These thresholdsare easily determined based on physical parameters, includingappropriate margins to avoid hysteresis.

However, traditional methods are not able to adapt to multiple criteria,dynamic user preferences, and changing network availability.

Several methods have been proposed to deal with multiple criteria, whichrely on definition of an appropriate cost function, utility function, orweighting of different metrics. The number of different parametersinvolved can be large, and these parameters must often be completelyspecified by an expert ahead of time. Additionally, often the differentparameters are not always available for a given network. Furthermore,when preferences change, the algorithm does not.

Therefore, there is a need for a network selection and verticalhandover, which can adapt to dynamically changing preferences andenvironmental conditions of the networks.

SUMMARY OF THE INVENTION

Accordingly, disclosed is a method for determining whether to performvertical handoff from a current communication network to one of aplurality of other communication networks. The method comprises thesteps of obtaining for each of the plurality of other communicationnetworks, a plurality of selection metrics, calculating for each of theplurality of other communication networks a predicted utility value fromat least the corresponding plurality of selection metrics using avariable kernel regression function, obtaining for the currentcommunication network a second plurality of selection metrics,calculating a second predicted utility value for the currentcommunication network from at least the corresponding second pluralityof selection metrics using a second variable kernel regression function,comparing each of the predicted utility values for each of the pluralityof other communication networks with the second predicted utility value;and switching to one of the plurality of other communication networkshaving the highest predicted utility value, if the highest predictedutility value is greater than the second predicted utility value. Theperiod of time in the future is different for each communication networkand is a function of a network specific handoff latency. Thecommunication network can be selected from 3G cellular, WLAN, and WiMax.

The method further comprises the steps of determining a switching costfor switching between the current communication network and each of theplurality of other communication networks, and switching to one of theplurality of other communication networks having the highest predictedutility value, if the highest predicted utility value is greater thanthe sum of the second predicted utility value, and the switching costfor switching between the current network and the communication networkwith the highest predicted utility.

The method further comprises the step of calculating an actual utilityvalue for the current communication network. The step of calculating theactual utility value comprises the sub-steps of mapping each of thesecond plurality of metrics to attribute preference values, multiplyingthe attribute preference values by a variable weighting factor andadding linearly each of the multiplied attribute preference values.

Alternatively, the step of calculating the actual utility value for thecurrent communication network comprises the step of evaluating thekernel regression function of the current network with current valuesobtained for each of the plurality of selection metrics.

The method further comprises a step of kernel learning. The kernellearning process comprises the steps of comparing the actual utilityvalue with the second predicted utility value, calculating a differencebetween the actual utility value with the second predicted utility valuebased upon the comparison and updating the second variable kernelregression function if the difference is greater than a loss tolerancevalue. Additionally, the loss tolerance value is updated based upon thedifference.

The variable kernel regression function is different for eachcommunication network.

The method further comprises the step of storing the plurality ofselection metrics for n previous periods of time.

The method further comprises the step of aging each of the previousplurality of selection metrics by multiplying a regression coefficientby an aging coefficient, where the aging coefficient is variable.

The selection metrics can include availability of a communicationnetwork, quality of service, and cost. The quality of service is afunction of packet delay. The cost is a function of a monetary cost andenergy cost. The selection metrics are periodically updated, by eithercalculating the metrics or receiving the metrics a priori and can bereceived by a network manager or managing entity. Alternative defaultselection metrics can be used. Additionally, the selection metrics caninclude at least information regarding a network policy. The networkpolicy information can include user classification, user priority,emergency needs, and network conditions.

Also disclosed is another method for determining whether to performvertical handoff from a current communication network to one of aplurality of other communication networks. The method comprisesobtaining for each of the plurality of other communication networks aplurality of selection metrics, calculating for each of the plurality ofother communication networks a predicted utility value from at least thecorresponding plurality of selection metrics using a variable kernelregression function, obtaining for the current communication network asecond plurality of selection metrics, calculating a second predictedutility value for the current communication network from at least thecorresponding second plurality of selection metrics using a secondvariable kernel regression function, determining all pendingapplications running a device, obtaining application thresholds for eachpending application, selecting an application threshold from theobtained application thresholds, calculating a difference between thesecond predicted utility value and each of the predicted utility values,comparing each of calculated differences with the selected applicationthreshold; and switching to a network having a highest predicted utilityand having a predicted utility greater than the selected applicationthreshold. The first and second predicted utility values are determinedfor a predetermined period of time in the future.

Each application threshold can be different from each other applicationthreshold based upon the particular application.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, benefits, and advantages of the presentinvention will become apparent by reference to the following figures,with like reference numbers referring to like structures across theviews, wherein:

FIG. 1 illustrates a flow diagram of a method of determining handoffaccording to an embodiment of the invention;

FIG. 2 illustrates a flow diagram of a method for calculating a utilityof a network;

FIG. 3 illustrates a learning process according to an embodiment of theinvention; and

FIG. 4 illustrates a flow chart of an handoff decision method accordingto a second embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a flow diagram of a method of determining handoffaccording to an embodiment of the invention. The method considersmultiple attributes and metrics for each network that a mobile device iscapable of using for communication. The method also accounts for anydynamic change in the preferences. The networks can be any availablecommunication network such as, but not limited to, 3G cellular, WLAN,and WiMax.

At Step 100, the attributes or metrics for each network is obtained. Themetrics can be calculated or are a priori known. The actual values foreach of the network attributes are not always known for all networks.For example, attributes of networks other than the current network maynot be known. However, in an embodiment several of the attributes andmetrics have default values. For example, a default packet delay for anetwork will be used if the actual packet delay is not known.Additionally, a default coverage range will also be used if the actualcoverage range is not known.

In one embodiment, the attributes are divided into three maincategories: availability, quality of service, and cost. ‘Availability’means satisfaction of basic connectivity requirements. In anotherembodiment, Availability is determined based upon the signal strength,signal strength RSS_(i)>minimum threshold Δ_(i). In another embodiment,other input information such as input about the signal strength,observed packet delay, stability period, user speed, and additionalinformation such as nominal coverage area, or coverage maps (ifavailable) can also be used. ‘Quality’ is typically measured in(available) bandwidth that a network can offer. The nominal bandwidth ofa network may be known a priori, but available bandwidth is hard ortime-consuming to measure. In the preferred embodiment, packet delay forthe network is used. In another embodiment, the average delays and delayvariance is used, as well as maximum allowable values. ‘Cost’ has twocomponents: Monetary cost and Energy cost. The Energy cost for a networkinterface is determined by two quantities: stationary energy (for justhaving the interface up) and transmission/reception energy. The Monetarycost is determined by the rate plan, and the cost per month, minute, orKB transferred

In another embodiment, another category of metrics is used: networkpolicies. A network policy includes short and/or long term policies suchas user classifications, user priority, emergency service needs andnetwork conditions.

At Step 110, the actual utility of the network is calculated. FIG. 2illustrates one method for calculating the actual utility, using amultiple attribute utility function. The utility function maps value ofthe attributes and metric to preference values. Specifically, themetrics are mapped to build a multi-attribute utility function, at Step200.

In one embodiment, the Availability utility function U_(A)(t) is definedas follows: U_(A,i)(t)=1, if RSS_(i)(t+ΔT_(i))>Δ_(i), U_(A)(t)=0otherwise. Quality utility function U_(Q)(t) as follows: U_(Q,i)(t)=1,if D_(i)(t+ΔT_(i))<d_(i), U_(Q,i)(t)=0. Cost utility function asfollows: U_(C)(t)=αU_(M)(t)+(1−α)U_(E)(t). At Step 205, weights aremultiplied to each preference value. The weights are variable to accountfor different hierarchy for each attribute. The weights are c1, c2, andc3. At Step 210, all of the weighted values are added together. Theoverall utility function for vertical handover is given by the linearcombination:

U(t)=c _(i) U _(A)(t)+c ₂ U _(Q)(t)+c ₃ U _(c)(t).

In one embodiment, an expected utility is determined for each of theattributes, using a multiple attribute expected utility function. Theexpected utility and a predicted utility (which will be described later)are determined for a predetermined time period in the future T+ΔT_(i).The predetermined time period is network specific and is a function of astability period. The stability period is equal to “make-uptime+handover latency”, or ΔT=T_(makeup)+L_(handover). Make-up time isthe time to make up the loss (in utility) due to loss of networkconnectivity during handoff latency.

The make-up time and handover latency is also network specific. Only ifan alternative network is predicted to be sufficiently better than thecurrent one for a period greater than the stability period is a handoffworthwhile. Therefore, the expected utility is calculated for a periodafter the stability period.

The expected utility for the availability is:

A _(i)(t)=EU_(A,i)(t)=P(RSS_(i)(t+ΔT _(i))>Δ_(i))  (1),

where P is the probability. The probability is based upon coverage maps,user speed and variance.

The expected utility for the Quality of Service is:

$\begin{matrix}{{{{Q_{i}(t)}\text{:} = {{EU}_{Q\;,i}(t)}} = {P\left( {{D_{i}\left( {t + {\Delta \; T_{i}}} \right)} < d_{i}} \right)}}{or}} & (2) \\{{Q_{i}(t)} = {\frac{{Ed}\left( {t + {\Delta \; T_{i\;}}} \right)}{D_{ave}^{Max}} + \frac{{var}\left( {d\left( {t + {\Delta \; T_{i}}} \right)} \right.}{D_{var}^{Max}}}} & (3)\end{matrix}$

The expected utility for the cost is:

C _(i)(t):=EU_(c)(t)=αEU_(M)(t)+(1−α)EU_(E)(t)  (4)

The overall expected utility is given by:

EU_(i)(t)=c ₁ A _(i)(t)+c ₂ Q(t)+c ₃ C(t)  (5)

The expected utility at a future time T+ΔT_(i) is used as a means topredict future utility.

In another embodiment, the actual utility of the current network can becalculated using a kernel regression function with the inputs for thekernel regression function being the obtained metrics for the currentnetwork.

At Step 120, the future utility for each network is predicted. In oneembodiment, the future or expected utility is predicted using equation 5(the multiple attribute expected utility function). In anotherembodiment, the determination uses a kernel learning process with theselection of a kernel “K” and a kernel regression function “f”. Thekernel learning process allows the method and prediction to adapt to achange in the environment or network condition. The kernel learningprocess will be described later in detail.

At Step 130, the cost of switching between networks is determined. Thecost of switching is a function of the stability period. The greater thestability period is, the higher the costs of switching.

At Step 140, a comparison of the expected utility EU_(i) of network i tothe expected utility of the current network, EU_(current), for eachalternative network i. The expected utility can be calculated usingeither the multiple attribute expect utility function or the kernallearning process with the kernal regression function. The switching costis denoted as γ_(i) for network i. If EU_(i)−γ_(i)>EU_(current), orequivalently: f(x_(t) ^(i))=EU_(i)>EU_(current)+γ_(i)=f(x_(t)^(current))+γ_(i), then hand off to network i, at Step 145, otherwisethe device stays connected to the current network, Step 150.

As noted above, the values and weights of the attributes and metrics canvary over time; therefore, the predicted utility must be dynamic andlearned based upon prior mapping of input to utility.

The kernel regression function for predicting the utility is variableand can be different for each network. Additionally, the kernelregression function for predicting the utility can also be varied basedupon a determined difference between an actual utility and an estimatedutility. The kernel regression function is used to predict the utilityof a network because all of the metrics, coefficients, loss tolerance,and expectations are not perfectly known.

The kernel learning process operates with X being defined as a set ofinputs, e.g., vector of the collected metrics for a network and Y beingdefined as the set of outcome (expected utility) values where Y=R. Rbeing real numbers. The mapping f: X→R is determined. A loss function l:R×Y→R given by l(f(x),y), is used to account for and penalize thedeviation of estimates f(x) from observed outcome labels y. The output fof the algorithm is a hypothesis. The set of all possible hypotheses isdenoted H. H is a Reproducing Kernel Hilbert Space (RKHS) induced by apositive semi-definite kernel k(.,.): X×X→R. This means that thereexists a kernel k: X×X→R and an inner product <•,•>_(H) such that (1) khas the reproducing property. <f,k(x,•)>_(H)=f(x), ∀xεX, and (2) H isthe closure of the span of all k(x,•), xεX.

In other words, the hypotheses space H, a Reproducing Kernel HibertSpace (RKHS), contains all functions ƒ which can be written as linearcombinations of kernel functions: for each fεH. Additionally the kernelregression can be written as:

$\begin{matrix}{{f(x)} = {\sum\limits_{i = 1}^{\infty}\; {\alpha_{i}{{k\left( {x_{i},x} \right)}.}}}} & (6)\end{matrix}$

where (x₁, y_(l)), . . . , (x_(n), y_(n)) x_(i)εX, y_(i)εY are theobserved (input,outcome) pairs, e.g. (metrics,utility) pairs.

The function “f” and its coefficients α_(i) in (6), are chosen tominimize a regularized risk:

$\begin{matrix}{{{R_{{reg},\lambda}\left\lbrack {f,S} \right\rbrack}\text{:} = \frac{1}{m}{\sum\limits_{i = 1}^{m}\; {l\left( {{f\left( x_{t} \right)},y_{t}} \right)}}} + {\frac{\lambda}{2}{f}_{H}^{2}}} & (7)\end{matrix}$

where the loss function is:

l(f(x),y):=max(0,|−y−f(x)|−ε).  (8)

This loss function is called “ε-insensitive loss”. E is a losstolerance.

The ε-insensitive loss function ignores small errors, i.e., if thedifference between the predicted value and the actual value is less thanthe tolerance, then the difference can be ignored. The advantage ofusing this loss function is that it creates a sparser kernel regressionfunction f, which is therefore less computationally intensive toevaluate, e.g. more coefficients are zero. In an embodiment, e can beadapted during the learning process.

The kernel k is defined in terms of the expected utility function EU(t)and its components A(t), Q(t) and C(t), which are given in formulas(1)-(5) above. In one embodiment, the starting point for the kernel isMapping Φ: X→R³ from observations (signal strength, coverage, delay,loss, jitter, energy usage) to

Φ(x _(t))=(A(t),Q(t),C(t))  (9)

The overall (expected) utility EU(t) is given as a linear combination ofthese vector components

EU(t)=c ₁ A(t)+c ₂ Q(t)+c ₃ C(t)=<c,Φ(x _(l))>  (10)

where c=(c₁, c₂, c₃) and Φ(x_(t)) is defined as above, the observationsand mapping value for the current network as baseline.

The mapping f: X→R, represents the expected utility and can be definedin terms of c=(c₁, c₂, c₃) and Φ(x_(t)) as

f(x):=<c,Φ(x)>  (11)

The kernel is defined as

k(x _(t) ,x):=<Φ(x _(t)),Φ(x)>.  (12)

In other words, the kernel regression function “f” is equivalent to themultiple attribute expected utility function.

x_(t) ^(i) represents the state of network i at time t, and f(x_(t)^(i))=<c,Φ(x_(t) ^(i))>=U_(i)(t).Additionally this equivalence can be written as:

f(x _(t) ^(i))=<c,Φ(x _(t) ^(ie))>=EU _(i)(t)=Σ_(i=1) ^(t−1)α_(i) k(x_(i) ,x)

Therefore, the kernel regression function “f”, representing thepredicted or expected utility, can be written as an expansion in termsof kernel k, without direct reference to the components A(t), Q(t) andC(t). An advantage of kernel methods is the kernel k is more compact andoften easier to store than the original mapping Φ(x_(i))=(A(i), Q(i),C(i)) or its components.

FIG. 3 illustrates the adaptive learning process for predicting theutility for each network. The utility is updated by sequentialapproximations f=(f₁, . . . , f_(m+1)), where f₁ is some arbitraryinitial hypothesis, e.g., f₁ is given by f₁≡0 (f₁(x)=0 for all xεX);f_(t),t>1, is the ‘hypothesis’ estimated after t−1 observations andl(f_(t), (x_(t), y_(t)) is the loss incurred by the learning algorithmwhen trying to predict y_(t) based on x_(t) and the previous examples((x₁, y₁), . . . , (x_(t−1), y_(t−1))).

At Step 300, a predicted value for the current network is compared withthe actual utility value that is determined in Step 110. A differencebetween the two values is calculated. The difference is compared with avariable loss tolerance, at Step 310. If the difference is less than theloss tolerance, the regression function is not updated, at Step 315.

On the other hand, if the difference is greater than the loss tolerance,the regression function is updated, at Steps 320 and 325. Step 320varies the coefficients as will be defined below and Step 325 varies theloss tolerance as defined herein.

The regression function is defined as:

f _(t+1):=(1−η_(t)λ)f _(t)−η_(t) l′(f _(t)(x _(t)),y _(t))k(x _(t),•)

with

f _(t)(x)=Σ_(i=1) ^(t−1)α_(i) k(x _(i) ,x),xεX.  (13)

The coefficients for the expansion of f_(t+1) at time t are calculatedas:

α_(t):=−η_(t) l′(f _(t)(x _(t)),y _(t)),i=t  (14)

α_(i):=(1−η_(t)λ)α_(i) ,i<t  (15)

η_(t)<1/λ is a learning parameter, where λ>0 is a penalty parameter thatregularizes the risk, by penalizing the norm of the kernel regressionfunction “f”. If λ>0 is large, the learning parameter η_(t)<1/λ issmaller, as are the resulting coefficients α_(i). The parameter λ isused to control the storage requirements for the kernel expansion.

As noted above, the loss tolerance, i.e., E-insensitive loss,

l(f(x),y):=max(0,|y−f(x)|−ε) can be variable. Therefore, the lossfunction is written

as

l(f(x),y):=max(0,|y−f(x)|−ε)+vε, for some 0<v<1.

Varying the value v varies the loss tolerance. In particular, v controlsthe fraction of points f(x_(i)) which have a loss exceeding the losstolerance ε.

The new coefficients α_(t) α_(i), i=1, . . . , t−1 in f_(t+1) and newloss tolerance ε are given by the following update equations:

$\begin{matrix}{\left( {\alpha_{i},\alpha_{t},ɛ} \right)\text{:} = \left\{ \begin{matrix}{\left( {{\left( {1 - {\lambda\eta}} \right)\alpha_{i}},{\eta \; {{sgn}\left( \delta_{t} \right)}},{ɛ + {\left( {1 - v} \right)\eta}}} \right),{{\delta_{t}} > \sigma}} \\{\left( {{\left( {1 - {\lambda\eta}} \right)\alpha_{i}},0,{ɛ - {\eta \; v}}} \right),{otherwise}}\end{matrix} \right.} & (16)\end{matrix}$

In an embodiment, the older input values, e.g., attributes, are agedsuch that the older values have less of an influence on the currentestimation than the newer attributes. For example, at time t, the α_(t)coefficient may be initialized to a non-zero value, and the coefficientsfor the t−1 earlier terms decay by a factor depending on η_(t).

In another embodiment, the decision to switch between networks, i.e.,handoff, is application based. For example, if an application isexpected to be used for a long period of time, an increase in apredicted utility from the current network to a new network could besmall. However, if the application will be used for a short period oftime, the increase in a predicted utility from the current network to anew network could be much larger to make the switch worthwhile. Inaccordance with this embodiment, multiple different utility thresholdsare used to determine whether to switch networks. The thresholds can bein “percentage increases” between networks. For example, if theapplication is streaming video for a movie, the threshold can be a 5%increase between networks (accounting for switching costs). If theapplication is a text message, the threshold can be a 30% or largerincrease between networks (accounting for switching costs).

In an embodiment, for different applications, the weights for themetrics are different.

FIG. 4 illustrates a flow chart of an handoff decision method accordingto the second embodiment of the invention. As illustrated, Steps 100-130are the same as the first embodiment of the invention and, therefore,will not be described again.

Once all of the future utilities are predicted, a determination of allof the current pending and running applications are made, at Step 400.Application threshold values are retrieved for all pending applications.At Step 410, the predicted utility value for the current network iscompared with each of the predicted utility values for the othernetworks and to calculate a utility difference for between the currentnetwork and each network. At Step 420, each utility difference iscompared with the application threshold value. In an embodiment, thesmallest threshold value among application threshold values for thepending application is selected for comparison. In another embodiment,largest threshold value among application threshold values for thepending application is selected for comparison. In another embodiment,an average of the application threshold values for the pendingapplications is selected for comparison.

If, at Step 420, a utility difference is larger than the selectedapplication threshold, then the other network remains a candidate forhandoff. The network that has the highest utility difference among theremaining candidates is selected for handoff and handoff occurs at Step425. If none of the utility differences are larger than the selectedapplication threshold, at Step 420, handoff does not occur, at Step 430.

The invention has been described herein with reference to a particularexemplary embodiment. Certain alterations and modifications may beapparent to those skilled in the art, without departing from the scopeof the invention. The exemplary embodiments are meant to beillustrative, not limiting of the scope of the invention, which isdefined by the appended claims.

What is claimed is:
 1. A system comprising: a mobile communicationsdevice configured to receive one or more first selection metrics and oneor more second selection metrics; a first network configured tocommunicate with the mobile communications device, wherein the one ormore first selection metrics correspond to the first network; a secondnetwork configured to communicate with the mobile communications device,wherein the one or more second selection metrics correspond to thesecond network; wherein the mobile communications device includes aprocessor configured to determine a first predicted utility value forthe first network based at least in part on the one or more firstselection metrics, wherein the first predicted utility value isdetermined using a first function corresponding to the first network ata first predetermined time period, wherein the processor is furtherconfigured to determine a second predicted utility value for the secondnetwork based at least in part on the one or more second selectionmetrics, wherein the second predicted utility value is determined usinga second function corresponding to the second network at a secondpredetermined time period, wherein the second function is specific tothe second network, wherein the first function is specific to the firstnetwork and is different than the second function, and wherein the firstpredetermined time period is different from the second predeterminedtime period, wherein the mobile communications device is configured toswitch from the first network to the second network if the secondpredicted utility value exceeds the first predicted utility value. 2.The system of claim 1, wherein the first function comprises a firstvariable kernel regression function and the second function comprises asecond variable kernel regression function.
 3. The system of claim 1,wherein the first predetermined time period is based at least in part ona first handoff latency specific to the first network.
 4. The system ofclaim 3, wherein the second predetermined time period is based at leastin part on a second handoff latency of the second network.
 5. The systemof claim 4, wherein the second predetermined time period is furtherbased at least in part on a make-up time, and wherein the make-up timecomprises an amount of time that network connectivity is lost during aswitch from the first network to the second network.
 6. The system ofclaim 1, wherein the processor is further configured to determine aswitching cost of switching from the first network to the second networkand determine a sum of the switching cost and the first predictedutility value, wherein the processor is configured to switch from thefirst network to the second network only if the second predicted utilityvalue is greater than the sum.
 7. The system of claim 1, wherein thefirst predicted utility value comprises an actual utility value based onone or more current values of the one or more first selection metrics.8. The system of claim 1, wherein the processor is further configured todetermine an actual utility value corresponding to the first network anda difference between the actual utility value and the first predictedutility value, wherein the processor is configured to update the firstfunction if the difference is greater than a loss tolerance value. 9.The system of claim 8, wherein the processor is configured to update theloss tolerance value based at least in part on the difference.
 10. Thesystem of claim 1, wherein the one or more first selection metricsinclude an availability of the first network, wherein the availabilityis based at least in part on a signal strength of the first network. 11.The system of claim 1, wherein the one or more first selection metricsinclude a quality of service of the first network, wherein the qualityof service is based at least in part on one or more of a packet delayassociated with the first network or a bandwidth of the first network.12. The system of claim 1, wherein the one or more first selectionmetrics include a cost of the first network, and wherein the costincludes at least one of a monetary cost for use of the first network oran energy cost associated with use of the first network.
 13. The systemof claim 24, wherein the one or more first selection metrics include anetwork policy of the first network, wherein the network policy includesone or more of a user classification, a user priority, an emergencyservice, or a network condition.
 14. The system of claim 1, wherein theprocessor is configured to identify an application running on the mobilecommunications device and an application threshold corresponding to theapplication; wherein the processor is configured to determine adifference between the first predicted utility value and the secondpredicted utility value and switch from the first network to the secondnetwork only if the second predicted utility value exceeds the firstpredicted utility value by at least the application threshold.
 15. Thesystem of claim 14, wherein the application threshold comprises asmallest application threshold of a plurality of application thresholdscorresponding to a plurality of applications running on the mobilecommunications device.
 16. The system of claim 14, wherein theapplication threshold comprises a largest application threshold of aplurality of application thresholds corresponding to a plurality ofapplications running on the mobile communications device.
 17. The systemof claim 14, wherein the application threshold comprises an averageapplication threshold of a plurality of application thresholdscorresponding to a plurality of applications running on the mobilecommunications device.
 18. The system of claim 14, wherein theapplication threshold comprises a percentage by which the secondpredicted utility value is to exceed the first predicted utility value.19. A system comprising: a mobile communications device configured torun an application; a first network configured to communicate with themobile communications device; and a second network configured tocommunicate with the mobile communications device, wherein the mobilecommunications device is configured to determine a first predictedutility value for the first network, wherein the first predicted utilityvalue is determined using a first function corresponding to the firstnetwork at a first predetermined time period, wherein the mobilecommunications device is further configured to determine a secondpredicted utility value for a second network, wherein the secondpredicted utility value is determined using a second functioncorresponding to the second network at a second predetermined timeperiod different from the first predetermined time period, wherein thesecond function is specific to the second network, and wherein the firstfunction is specific to the first network and is different from thesecond function, wherein the mobile communications device is furtherconfigured to determine a difference between the first predicted utilityvalue and the second predicted utility value, identify an applicationthreshold associated with the application running on the mobilecommunications device, and switch from the first network to the secondnetwork if the second predicted utility value exceeds the firstpredicted utility value by at least the application threshold.
 20. Asystem comprising a communications device configured to receive one ormore first selection metrics and one or more second selection metrics; afirst network configured to communicate with the communications device,wherein the one or more first selection metrics correspond to the firstnetwork; a second network configured to communicate with thecommunications device, wherein the one or more second selection metricscorrespond to the second network; wherein the communications device isconfigured to determine a first predicted utility value for the firstnetwork based at least in part on the one or more first selectionmetrics, wherein the second predicted utility value is determined usinga second function corresponding to the second network at a secondpredetermined time period, and wherein the first predetermined timeperiod is different from the second predetermined time period, whereinthe mobile communications device is configured to switch from the firstnetwork to the second network if the second predicted utility valueexceeds the first predicted utility value.